In recent years, the success rate of solving major criminal cases through big data has been greatly improved. The analysis of multimodal big data plays a key role in the detection of suspects. However, the traditional multiexposure image fusion methods have low efficiency and are largely time-consuming due to the artifact effect in the image edge and other sensitive factors. Therefore, this paper focuses on the suspect multiexposure image fusion. The self-coding neural network based on deep learning has become a hotspot in the research of data dimension reduction, which can effectively eliminate the irrelevant and redundant learning data. In the case of limited field depth, due to the limited focusing depth of the camera, the focusing plane cannot obtain the global clear image of the target in the depth scene, which is prone to defocusing and blurring phenomena. Therefore, this paper proposes a multifocus image fusion based on a sparse denoising autoencoder neural network. To realize an unsupervised end-to-end fusion network, the sparse denoising autoencoder neural network is adopted to extract features and learn fusion rules and reconstruction rules simultaneously. The initial decision graph of the multifocus image is taken as a prior input to learn the rich detailed information of the image. The local strategy is added to the loss function to ensure that the image is restored accurately. The results show that this method is superior to the state-of-the-art fusion methods.

中文翻译：

---

基于稀疏去噪自动编码器神经网络的可疑多焦点图像融合技术在警察多式联运大数据分析中的应用

近年来，通过大数据解决重大刑事案件的成功率已大大提高。多模式大数据分析在发现嫌疑犯中起关键作用。然而，由于图像边缘中的伪影效应和其他敏感因素，传统的多重曝光图像融合方法效率低下，并且非常耗时。因此，本文重点关注可疑的多重曝光图像融合。基于深度学习的自编码神经网络已成为数据降维研究的热点，可以有效消除无关紧要的学习数据。在景深有限的情况下，由于相机的对焦深度有限，对焦平面无法获得景深场景中目标的全局清晰图像，容易造成散焦和模糊现象。因此，本文提出了一种基于稀疏去噪自动编码器神经网络的多焦点图像融合方法。为了实现无监督的端到端融合网络，采用稀疏去噪自动编码器神经网络提取特征，同时学习融合规则和重构规则。将多焦点图像的初始决策图作为优先输入，以学习图像的丰富详细信息。局部策略已添加到损失函数，以确保准确还原图像。结果表明，该方法优于最新的融合方法。采用稀疏去噪自动编码器神经网络提取特征，同时学习融合规则和重构规则。将多焦点图像的初始决策图作为优先输入，以学习图像的丰富详细信息。局部策略已添加到损失函数，以确保准确还原图像。结果表明，该方法优于最新的融合方法。采用稀疏去噪自动编码器神经网络提取特征，同时学习融合规则和重构规则。将多焦点图像的初始决策图作为优先输入，以学习图像的丰富详细信息。局部策略已添加到损失函数，以确保准确还原图像。结果表明，该方法优于最新的融合方法。